为四足机器人提供分层视觉导航系统,具有脚本适应学习学习.
Junli Ren1, Yingru Dai1, Bowen Liu1
1Department of Electronic Engineering, Tsinghua University, Beijing 100084, China.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
本研究介绍了四足机器人等级视觉导航系统. 该系统通过将自适应式立足计划与机动控制相结合,使混乱环境中的有效导航成为可能.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 有腿的机器人提供了动态运动能力,可以在复杂的地形上进行导航.
- 在混乱的环境中,高效的导航和动态利用仍然是腿类机器人的重大挑战.
- 目前的系统往往难以实时适应不可预测的障碍和地形变化.
研究的目的:
- 为四足机器人开发一种新的等级视觉导航系统.
- 在混乱和动态环境中提高导航效率和动态控制.
- 为了使机器人能够自主调整脚的位置,以实现强大的运动.
主要方法:
- 一个分层系统,将一个高层次的端到端导航政策与一个低层次的足迹适应政策相结合.
- 高层政策产生最佳路径,避免障碍.
- 低级政策利用自动注释的监督学习来进行自适应的运动控制和可行的脚位.
主要成果:
- 该系统在动态和混乱的环境中展示了高效的导航.
- 在没有事先的环境信息的情况下,成功避免了障碍和目标方法.
- 在模拟和现实场景中的实验验证实了系统的有效性.
结论:
- 拟议的层次视觉导航系统显著提高了四足机器人的导航效率.
- 整合自适应的立足计划可以提高机器人处理具有挑战性的地形的能力.
- 这种方法为非结构化和动态环境中的自主导航提供了强大的解决方案.
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